



import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	Any:
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__					=       k.replace(UpperCamelCase_			,							UpperCamelCase_						)

    if k.startswith('''encoder'''						):
        A__					=       k.replace('''.attn'''			,							'''.self_attn'''						)
        A__					=       k.replace('''norm1'''			,							'''self_attn_layer_norm'''						)
        A__					=       k.replace('''norm2'''			,							'''final_layer_norm'''						)
    elif k.startswith('''decoder'''						):
        A__					=       k.replace('''norm1'''			,							'''self_attn_layer_norm'''						)
        A__					=       k.replace('''norm2'''			,							'''encoder_attn_layer_norm'''						)
        A__					=       k.replace('''norm3'''			,							'''final_layer_norm'''						)
    return k



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	Tuple:
    A__					=       [
        '''model.encoder.layernorm_embedding.weight''',
        '''model.encoder.layernorm_embedding.bias''',
        '''model.decoder.layernorm_embedding.weight''',
        '''model.decoder.layernorm_embedding.bias''',
    ]
    for k in keys:
        A__					=       sd.pop(UpperCamelCase_						)
        A__					=       k.replace('''layernorm_embedding'''			,							'''layer_norm'''						)
        assert new_k not in sd
        A__					=       v


_lowercase    	=		["START"]



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[Any]			,							UpperCamelCase_					:							Tuple						)->	str:
    A__					=       torch.load(UpperCamelCase_			,							map_location='''cpu'''						)
    A__					=       model['''model''']
    A__					=       BlenderbotConfig.from_json_file(UpperCamelCase_						)
    A__					=       BlenderbotForConditionalGeneration(UpperCamelCase_						)
    A__					=       m.model.state_dict().keys()
    A__					=       []
    A__					=       {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        A__					=       rename_state_dict_key(UpperCamelCase_						)
        if new_k not in valid_keys:
            failures.append([k, new_k]						)
        else:
            A__					=       v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(UpperCamelCase_						)
    m.model.load_state_dict(UpperCamelCase_			,							strict=UpperCamelCase_						)
    m.half()
    m.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
   parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
   parser.add_argument(
       "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
   )
   _lowercase    	=		parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

def 			lowercase(	UpperCamelCase_ = 100       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      n * (n + 1) * (2 * n + 1) / 6
 UpperCamelCase			  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares       )


if __name__ == "__main__":
     print(F'''{solution() = }''')
